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  • 1
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    DR-NASNet: Automated System to Detect and Classify Diabetic Retinopathy Severity Using Improved Pretrained NASNet Model
    MDPI AG ; 2023
    In:  Diagnostics Vol. 13, No. 16 ( 2023-08-10), p. 2645-
    Details
    In: Diagnostics, MDPI AG, Vol. 13, No. 16 ( 2023-08-10), p. 2645-
    Abstract: Diabetes is a widely spread disease that significantly affects people’s lives. The leading cause is uncontrolled levels of blood glucose, which develop eye defects over time, including Diabetic Retinopathy (DR), which results in severe visual loss. The primary factor causing blindness is considered to be DR in diabetic patients. DR treatment tries to control the disease’s severity, as it is irreversible. The primary goal of this effort is to create a reliable method for automatically detecting the severity of DR. This paper proposes a new automated system (DR-NASNet) to detect and classify DR severity using an improved pretrained NASNet Model. To develop the DR-NASNet system, we first utilized a preprocessing technique that takes advantage of Ben Graham and CLAHE to lessen noise, emphasize lesions, and ultimately improve DR classification performance. Taking into account the imbalance between classes in the dataset, data augmentation procedures were conducted to control overfitting. Next, we have integrated dense blocks into the NASNet architecture to improve the effectiveness of classification results for five severity levels of DR. In practice, the DR-NASNet model achieves state-of-the-art results with a smaller model size and lower complexity. To test the performance of the DR-NASNet system, a combination of various datasets is used in this paper. To learn effective features from DR images, we used a pretrained model on the dataset. The last step is to put the image into one of five categories: No DR, Mild, Moderate, Proliferate, or Severe. To carry this out, the classifier layer of a linear SVM with a linear activation function must be added. The DR-NASNet system was tested using six different experiments. The system achieves 96.05% accuracy with the challenging DR dataset. The results and comparisons demonstrate that the DR-NASNet system improves a model’s performance and learning ability. As a result, the DR-NASNet system provides assistance to ophthalmologists by describing an effective system for classifying early-stage levels of DR.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics13162645
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 2
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    Effects of Different Biochars on Wheat Growth Parameters, Yield and Soil Fertility Status in a Silty Clay Loam Soil
    MDPI AG ; 2019
    In:  Molecules Vol. 24, No. 9 ( 2019-05-09), p. 1798-
    Details
    In: Molecules, MDPI AG, Vol. 24, No. 9 ( 2019-05-09), p. 1798-
    Abstract: The conversion of organic wastes into biochar via the pyrolysis technique could be used to produce soil amendments useful as a source of plant nutrients. In this study, we investigated the effects of fruit peels and milk tea waste-derived biochars on wheat growth, yield, root traits, soil enzyme activities and nutrient status. Eight amendment treatments were tested: no amendment (CK), chemical fertilizer (CF), banana peel biochar 1% (BB1 + CF), banana peel biochar 2% (BB2 + CF), orange peel biochar 1% (OB1 + CF), orange peel biochar 2% (OB2 + CF), milk tea waste biochar 1% (TB1 + CF) and milk tea waste biochar 2% (TB2 + CF). The results indicated that chlorophyll values, plant height, grain yield, dry weight of shoot and root were significantly (p 〈 0.05) increased for the TB2 + CF treatment as compared to other treatments. Similarly, higher contents of nutrients in grains, shoots and roots were observed for TB2 + CF: N (61.3, 23.3 and 7.6 g kg−1), P (9.2, 10.4 and 8.3 g kg−1) and K (9.1, 34.8 and 4.4 g kg−1). Compared to CK, the total root length (41.1%), surface area (56.5%), root volume (54.2%) and diameter (78.4%) were the greatest for TB2 + CF, followed by BB2 + CF, OB2 + CF, TB1 + CF, BB1 + CF, OB1 + CF and CF, respectively. However, BB + CF and OB + CF treatments increased β-glucosidase and dehydrogenase, but not urease activity, as compared to the TB + CF amendment, while all enzyme activity decreased with the increased biochar levels. We concluded that the conversion of fruit peels and milk tea waste into biochar products contribute the benefits of environmental and economic issues, and should be tested as soil amendments combined with chemical fertilizers for the improvement of wheat growth and grain yield as well as soil fertility status under field conditions.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    URL: Article
    DOI: 10.3390/molecules24091798
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2008644-1
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  • 3
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    Smart Patch for Skin Temperature: Preliminary Study to Evaluate Psychometrics and Feasibility
    MDPI AG ; 2021
    In:  Sensors Vol. 21, No. 5 ( 2021-03-06), p. 1855-
    Details
    In: Sensors, MDPI AG, Vol. 21, No. 5 ( 2021-03-06), p. 1855-
    Abstract: There is a need for continuous, non-invasive monitoring of biological data to assess health and wellbeing. Currently, many types of smart patches have been developed to continuously monitor body temperature, but few trials have been completed to evaluate psychometrics and feasibility for human subjects in real-life scenarios. The aim of this feasibility study was to evaluate the reliability, validity and usability of a smart patch measuring body temperature in healthy adults. The smart patch consisted of a fully integrated wearable wireless sensor with a multichannel temperature sensor, signal processing integrated circuit, wireless communication feature and a flexible battery. Thirty-five healthy adults were recruited for this test, carried out by wearing the patches on their upper chests for 24 h and checking their body temperature six times a day using infrared forehead thermometers as a gold standard for testing validity. Descriptive statistics, one-sampled and independent t-tests, Pearson’s correlation coefficients and Bland-Altman plot were examined for body temperatures between two measures. In addition, multiple linear regression, receiver operating characteristic (ROC) and qualitative content analysis were conducted. Among the 35 participants, 29 of them wore the patch for over 19 h (dropout rate: 17.14%). Mean body temperature measured by infrared forehead thermometers and smart patch ranged between 32.53 and 38.2 °C per person and were moderately correlated (r = 0.23–0.43) overall. Based on a Bland-Altman plot, approximately 94% of the measurements were located within one standard deviation (upper limit = 4.52, lower limit = −5.82). Most outliers were identified on the first measurement and were located below the lower limit. It is appropriate to use 37.5 °C in infrared forehead temperature as a cutoff to define febrile conditions. Users’ position while checking and ambient temperature and humidity are not affected to the smart patch body temperature. Overall, the participants showed high usability and satisfaction on the survey. Few participants reported discomfort due to limited daily activity, itchy skin or detaching concerns. In conclusion, epidermal electronic sensor technologies provide a promising method for continuously monitoring individuals’ body temperatures, even in real-life situations. Our study findings show the potential for smart patches to monitoring non-febrile condition in the community.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s21051855
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2052857-7
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  • 4
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    VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions
    MDPI AG ; 2023
    In:  Systems Vol. 11, No. 8 ( 2023-08-21), p. 436-
    Details
    In: Systems, MDPI AG, Vol. 11, No. 8 ( 2023-08-21), p. 436-
    Abstract: Data sharing with additional devices across wireless networks is made simple and advantageous by the Internet of Things (IoT), an emerging technology. However, IoT systems are more susceptible to cyberattacks because of their continued growth and technological advances, which could lead to powerful assaults. An intrusion detection system is one of the key defense mechanisms for information and communications technology. The primary shortcomings that plague current IoT security frameworks are their inability to detect intrusions properly, their substantial latency, and their prolonged processing time and delay. Therefore, this work develops a clever and innovative security architecture called Vectorization-Based Boost Quantized Network (VBQ-Net) for protecting IoT networks. Here, a Vector Space Bag of Words (VSBW) methodology is used to reduce the dimensionality of features and identify a key characteristic from the featured data. In addition, a brand-new classification technique, called Boosted Variance Quantization Neural Networks (BVQNNs), is used to classify the different types of intrusions using a weighted feature matrix. A Multi-Hunting Reptile Search Optimization (MH-RSO) algorithm is employed during categorization to calculate the probability value for selecting the right choices while anticipating intrusions. In this study, the most well-known and current datasets, such as IoTID-20, IoT-23, and CIDDS-001, are used to validate and evaluate the effectiveness of the proposed methodology. By evaluating the proposed approach on standard IoT datasets, the study seeks to address the limitations of current IoT security frameworks and provide a more effective defense mechanism against cyberattacks on IoT systems.
    Type of Medium: Online Resource
    ISSN: 2079-8954
    URL: Article
    DOI: 10.3390/systems11080436
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2663185-4
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  • 5
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    Flexible PI-Based Plant Drought Stress Sensor for Real-Time Monitoring System in Smart Farm
    MDPI AG ; 2018
    In:  Electronics Vol. 7, No. 7 ( 2018-07-16), p. 114-
    Details
    In: Electronics, MDPI AG, Vol. 7, No. 7 ( 2018-07-16), p. 114-
    Abstract: Plant growth and development are negatively affected by a wide range of external stresses, including water deficits. Especially, plants generally reduce the stomatal aperture to decrease transpiration levels upon drought stress. Advanced technologies, such as wireless communications, the Internet of things (IoT), and smart sensors have been applied to practical smart farming and indoor planting systems to monitor plants’ signals effectively. In this study, we develop a flexible polyimide (PI)-based sensor for real-time monitoring of water conditions in tobacco plants. The stoma response, by which a plant adjusts to drought stress to maintain homeostasis, can be confirmed through the examination of evaporated water. Using a flexible PI-based sensor, a plant’s response variation is translated into an electrical signal. The sensors are integrated with a Bluetooth (BLE) module and a processing module and show potential as smart real-time water sensors in smart farms.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics7070114
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2662127-7
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  • 6
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    High-Performance Non-Volatile InGaZnO Based Flash Memory Device Embedded with a Monolayer Au Nanoparticles
    MDPI AG ; 2021
    In:  Nanomaterials Vol. 11, No. 5 ( 2021-04-24), p. 1101-
    Details
    In: Nanomaterials, MDPI AG, Vol. 11, No. 5 ( 2021-04-24), p. 1101-
    Abstract: Non-volatile memory (NVM) devices based on three-terminal thin-film transistors (TFTs) have gained extensive interest in memory applications due to their high retained characteristics, good scalability, and high charge storage capacity. Herein, we report a low-temperature ( 〈 100 °C) processed top-gate TFT-type NVM device using indium gallium zinc oxide (IGZO) semiconductor with monolayer gold nanoparticles (AuNPs) as a floating gate layer to obtain reliable memory operations. The proposed NVM device exhibits a high memory window (ΔVth) of 13.7 V when it sweeps from −20 V to +20 V back and forth. Additionally, the material characteristics of the monolayer AuNPs (floating gate layer) and IGZO film (semiconductor layer) are confirmed using transmission electronic microscopy (TEM), atomic force microscopy (AFM), and x-ray photoelectron spectroscopy (XPS) techniques. The memory operations in terms of endurance and retention are obtained, revealing highly stable endurance properties of the device up to 100 P/E cycles by applying pulses (±20 V, duration of 100 ms) and reliable retention time up to 104 s. The proposed NVM device, owing to the properties of large memory window, stable endurance, and high retention time, enables an excellent approach in futuristic non-volatile memory technology.
    Type of Medium: Online Resource
    ISSN: 2079-4991
    URL: Article
    DOI: 10.3390/nano11051101
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662255-5
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